mollymr305 / mnist-mc-dropout
model uncertainty using mc dropout
☆20Updated 5 years ago
Alternatives and similar repositories for mnist-mc-dropout:
Users that are interested in mnist-mc-dropout are comparing it to the libraries listed below
- Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference, Gal et al. 2015☆36Updated 6 years ago
- Implementations of the ICML 2017 paper (with Yarin Gal)☆38Updated 7 years ago
- Code for Deep Bayesian Active Learning (ICML 2017)☆112Updated 6 years ago
- An implementation of "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" (http://arxiv.org/abs/1612.01474)☆34Updated 8 years ago
- ☆25Updated 2 years ago
- Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Ba…☆31Updated 3 years ago
- Uncertainty interpretations of the neural network☆32Updated 6 years ago
- Repository with code for paper "Inhibited Softmax for Uncertainty Estimation in Neural Networks"☆25Updated 5 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆140Updated 7 years ago
- Uncertainty estimation on Mnist dataset☆23Updated 7 years ago
- Code for DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN☆15Updated 6 years ago
- Train a simple convnet on the MNIST dataset and evaluate the BALD acquisition function☆16Updated 7 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆59Updated 6 years ago
- ☆81Updated 7 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Updated 8 years ago
- Code for our paper "CliqueCNN: Deep Unsupervised Exemplar Learning" https://arxiv.org/abs/1608.08792☆22Updated 7 years ago
- My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"☆135Updated 7 years ago
- Code for "Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference" (NeurIPS Bayesian Deep Learning W…☆24Updated 5 years ago
- Implementation of Ladder Network in PyTorch.☆45Updated 7 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- Regularized marginalized Stacked Denoising Autoencoders for Domain Adaptation☆12Updated 7 years ago
- Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood☆94Updated 7 years ago
- This is the code for our paper: Semi-Supervised Learning With GANs: Revisiting Manifold Regularization (ICLR 2018)☆44Updated 5 years ago
- Implementation of Visual Feature Attribution using Wasserstein GANs (VAGANs, https://arxiv.org/abs/1711.08998) in PyTorch☆93Updated last year
- Pytorch implementation of Adaptative Dropout a.ka Standout.☆12Updated 6 years ago
- contains the code for models in the paper Robust, Deep and Inductive Anomaly Detection☆34Updated 7 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 4 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 4 years ago
- Denoising Adversairal Autoencoders☆40Updated 7 years ago
- ☆13Updated 6 years ago